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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Forecasting weekly reference evapotranspiration using Auto Encoder Decoder Bidirectional LSTM model hybridized with a Boruta-CatBoost input optimizer
Masoud Karbasi, Mehdi Jamei, Mumtaz Ali, et al.
Computers and Electronics in Agriculture (2022) Vol. 198, pp. 107121-107121
Closed Access | Times Cited: 63

Showing 1-25 of 63 citing articles:

Deep learning based computer vision approaches for smart agricultural applications
V. G. Dhanya, A. Subeesh, Nand Lal Kushwaha, et al.
Artificial Intelligence in Agriculture (2022) Vol. 6, pp. 211-229
Open Access | Times Cited: 159
An interpretable machine learning approach based on DNN, SVR, Extra Tree, and XGBoost models for predicting daily pan evaporation
Ali El Bilali, Taleb Abdeslam, Ayoub Nafii, et al.
Journal of Environmental Management (2022) Vol. 327, pp. 116890-116890
Closed Access | Times Cited: 103
Applications of XGBoost in water resources engineering: A systematic literature review (Dec 2018–May 2023)
Majid Niazkar, Andrea Menapace, Bruno Brentan, et al.
Environmental Modelling & Software (2024) Vol. 174, pp. 105971-105971
Closed Access | Times Cited: 73
Future trends of reference evapotranspiration in Sicily based on CORDEX data and Machine Learning algorithms
Fabio Di Nunno, Francesco Granata
Agricultural Water Management (2023) Vol. 280, pp. 108232-108232
Open Access | Times Cited: 46
Research on Water Resource Modeling Based on Machine Learning Technologies
Liu Ze, Jingzhao Zhou, Xiaoyang Yang, et al.
Water (2024) Vol. 16, Iss. 3, pp. 472-472
Open Access | Times Cited: 21
Innovative approach for predicting daily reference evapotranspiration using improved shallow and deep learning models in a coastal region: A comparative study
Hussam Eldin Elzain, Osman Abdalla, Mohammed Abdallah, et al.
Journal of Environmental Management (2024) Vol. 354, pp. 120246-120246
Closed Access | Times Cited: 18
Improving daily reference evapotranspiration forecasts: Designing AI-enabled recurrent neural networks based long short-term memory
Mumtaz Ali, Jesu Vedha Nayahi, Erfan Abdi, et al.
Ecological Informatics (2025), pp. 102995-102995
Open Access | Times Cited: 2
A review of the Artificial Intelligence (AI) based techniques for estimating reference evapotranspiration: Current trends and future perspectives
Pooja Goyal, Sunil Kumar, Rakesh Sharda
Computers and Electronics in Agriculture (2023) Vol. 209, pp. 107836-107836
Closed Access | Times Cited: 40
Development of a TVF-EMD-based multi-decomposition technique integrated with Encoder-Decoder-Bidirectional-LSTM for monthly rainfall forecasting
Mehdi Jamei, Mumtaz Ali, Anurag Malik, et al.
Journal of Hydrology (2023) Vol. 617, pp. 129105-129105
Closed Access | Times Cited: 37
Hybrid machine learning system based on multivariate data decomposition and feature selection for improved multitemporal evapotranspiration forecasting
Jinwook Lee, Sayed M. Bateni, Changhyun Jun, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 135, pp. 108744-108744
Closed Access | Times Cited: 8
Multi-step ahead forecasting of electrical conductivity in rivers by using a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model enhanced by Boruta-XGBoost feature selection algorithm
Masoud Karbasi, Mumtaz Ali, Sayed M. Bateni, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 8
Multi-Step Forecasting of Meteorological Time Series Using CNN-LSTM with Decomposition Methods
Eluã Ramos Coutinho, Jonni Guiller Ferreira Madeira, Dérick G. F. Borges, et al.
Water Resources Management (2025)
Closed Access | Times Cited: 1
A novel hybrid modeling approach based on empirical methods, PSO, XGBoost, and multiple GCMs for forecasting long-term reference evapotranspiration in a data scarce-area
Ali El Bilali, Abdessamad Hadri, Abdeslam Taleb, et al.
Computers and Electronics in Agriculture (2025) Vol. 232, pp. 110106-110106
Closed Access | Times Cited: 1
Simulation of daily maize evapotranspiration at different growth stages using four machine learning models in semi-humid regions of northwest China
Zongjun Wu, Ningbo Cui, Daozhi Gong, et al.
Journal of Hydrology (2022) Vol. 617, pp. 128947-128947
Closed Access | Times Cited: 35
Performance of machine learning algorithms for multi-step ahead prediction of reference evapotranspiration across various agro-climatic zones and cropping seasons
Nehar Mandal, Kironmala Chanda
Journal of Hydrology (2023) Vol. 620, pp. 129418-129418
Closed Access | Times Cited: 19
Exploring interpretable and non-interpretable machine learning models for estimating winter wheat evapotranspiration using particle swarm optimization with limited climatic data
Xin Zhao, Lei Zhang, Ge Zhu, et al.
Computers and Electronics in Agriculture (2023) Vol. 212, pp. 108140-108140
Closed Access | Times Cited: 18
Hybrid machine learning and deep learning models for multi-step-ahead daily reference evapotranspiration forecasting in different climate regions across the contiguous United States
Mohammad Valipour, Helaleh Khoshkam, Sayed M. Bateni, et al.
Agricultural Water Management (2023) Vol. 283, pp. 108311-108311
Open Access | Times Cited: 17
Daily Scale River Flow Forecasting Using Hybrid Gradient Boosting Model with Genetic Algorithm Optimization
Hüseyin Çağan Kılınç, Iman Ahmadianfar, Vahdettin Demir, et al.
Water Resources Management (2023) Vol. 37, Iss. 9, pp. 3699-3714
Open Access | Times Cited: 17
Comparative analysis of different machine learning algorithms for predicting trace metal concentrations in soils under intensive paddy cultivation
Mehmet Taşan, Yusuf Demir, Sevda Taşan, et al.
Computers and Electronics in Agriculture (2024) Vol. 219, pp. 108772-108772
Closed Access | Times Cited: 6
Improving Short-term Daily Streamflow Forecasting Using an Autoencoder Based CNN-LSTM Model
Umar Muhammad Mustapha Kumshe, Z.M. Abdulhamid, Baba Ahmad Mala, et al.
Water Resources Management (2024) Vol. 38, Iss. 15, pp. 5973-5989
Closed Access | Times Cited: 6
A review of recent advances and future prospects in calculation of reference evapotranspiration in Bangladesh using soft computing models
Md Mahfuz Alam, Mst. Yeasmin Akter, Abu Reza Md. Towfiqul Islam, et al.
Journal of Environmental Management (2023) Vol. 351, pp. 119714-119714
Closed Access | Times Cited: 16
Multi-step ahead hourly forecasting of air quality indices in Australia: Application of an optimal time-varying decomposition-based ensemble deep learning algorithm
Mehdi Jamei, Mumtaz Ali, Changhyun Jun, et al.
Atmospheric Pollution Research (2023) Vol. 14, Iss. 6, pp. 101752-101752
Closed Access | Times Cited: 14
Vector Autoregression Model-Based Forecasting of Reference Evapotranspiration in Malaysia
Phon Sheng Hou, Lokman Mohd Fadzil, Selvakumar Manickam, et al.
Sustainability (2023) Vol. 15, Iss. 4, pp. 3675-3675
Open Access | Times Cited: 13
Explainable hybrid deep learning and Coronavirus Optimization Algorithm for improving evapotranspiration forecasting
Angela Robledo Troncoso-García, Isabel Sofía Brito, Alicia Troncoso, et al.
Computers and Electronics in Agriculture (2023) Vol. 215, pp. 108387-108387
Closed Access | Times Cited: 13
Statistical and deep learning models for reference evapotranspiration time series forecasting: A comparison of accuracy, complexity, and data efficiency
Arman Ahmadi, André Daccache, Mojtaba Sadegh, et al.
Computers and Electronics in Agriculture (2023) Vol. 215, pp. 108424-108424
Open Access | Times Cited: 13
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